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Friday, 22 June 2018

One of the coolest things about Neo4j is just the sheer breadth and diversity of applications that we see for connected data and graph databases out there. I think I have said it before, but it truly continues to baffle me. Very frequently, I will have a morning conversation with a user about battling financial fraud, a lunch conversation about using graphs in biotech to fight world hunger, and an afternoon conversation about real time recommender systems in retail. And of course finish it of with a beergraph conversation in the evening :) ...

Really - it's just amazing. And the next podcast episode is a true testimony to that. I got to have a chat with a lovely lady all the way over in Canada recently, Estelle Joubert from Dalhousie University. She and her team have been using Neo4j in her amazing field of research, which is all about understanding how music and opera came to be what they are today in a historical perspective. She is best at explaining it herself - so here's our chat:

Here's the transcript of our conversation:

RVB: 00:01:20.209 Hello, everyone. My name is Rik, Rik Van Bruggen from Neo4J, and tonight I am joined by a guest on our podcast all the way from Canada, someone that has been working with, and experimenting with, Neo4j for quite some time in a very interesting domain that I hadn't heard of before. And that's Estelle Joubert from Dalhousie University. Hi, Estelle.

Friday, 15 June 2018

In the previous blogpost I imported the Open Beer Database into Neo4j and added some new fancy spatial data to it. Now in this post I would like to explore that data. As a reminder, you can find the full

Thursday, 14 June 2018

Recently, I gave a talk at the Amsterdam, Brussels and London Neo4j meetups about some of the new and exciting features in Neo4j 3.4. While preparing for it, I was looking for material and I found some very cool stuff that powerfully explains the new features. The best resource is probably this post by Ryan Boyd, and the video that goes with it:

Ryan does a great job at explaining the new features, and goes into some detail on the new temporal and spatial data types that you can now use in Neo4j 3.4. You can explore these new features yourself by accessing the Neo4j Sandbox developed specifically for this purpose. Or you can just do what I did, and use the Neo4j Desktop to spin up a Neo4j instance, and access the "guide". You do that by typing

:play https://guides.neo4j.com/sandbox/3.4/index.html

into the Neo4j browser, and then you can access the entire guide, add some data to your dataset, and play around.

Friday, 8 June 2018

Here's a podcast episode that I have been wanting/needing to publish for a long time .Jeffrey A. Miller works as a Senior Consultant in Columbus, Ohio as a consultant in effective software development practices with lots of organisations. Jeffrey has delivered presentations at regional technical conferences and user groups on topics including Neo4j technology, knowledge management, and humanitarian healthcare projects - and that of course became a great setup for our conversation.

Also - I found this really interesting: Jeffrey and his wife, Brandy, are aspiring adoptive parents and have written a fun children’s book called “Skeeters” with proceeds supporting adoption. Learn more about the project at http://skeeterbooks.com/.

Friday, 1 June 2018

Over the past couple of weeks, I have been discussing and showing Neo4j's new Bloom graph discovery and visualization product to everyone that would have a moment to spare. It's soooo much fun to show a tool you love, and Bloom is definitely one of those. And I have also recorded some odf these demo-sessions - you can find part 1, part 2 and part 3 of these recordings on this blog. All of these recordings use my (in)famous Belgian Beergraph dataset - and that's all good fun...

But of course, exploring a beergraph is not really a "business-y" use case. So I decided I would record a Bloom demo using a realistic dataset that centers around using Neo4j for Fraud Detection purposes. You will find all of the important concepts of the Beergraph demos here as well:

navigating the graph using graph patterns

using nifty selection / deselection techniques to only show what you need in the graph